Equipment can become worn over time and, eventually, fail. For example, blades in turbines may develop spalls or cracks over time, which can lead to catastrophic failure of the turbines and/or significant downtime of the turbines if the damage is not discovered sufficiently early to avoid significant repair or replacement of parts in the turbines. Some known systems and methods can visually inspect the components of equipment in order to identify damage to the equipment.
But, these systems and methods have certain faults. As one example, the characterization of the damage appearing in images or video of the equipment can be highly subjective and prone to error. As another example, determination of the severity and/or likely spread of the damage can require a significant amount of information on the materials in the equipment, the environmental conditions to which the materials were exposed, the operating conditions in which the equipment operated, etc., may need to be known to accurately identify, characterize, and/or predict upcoming growth of the damage. This information may not be available for the automated analysis and/or prediction of upcoming growth or changes in damage to the equipment.